InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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Google Adds Cycle-Level Kernel Profiling to XProf
Google has added a Kernel Profiling suite to XProf. This is its open-source profiler for TPU workloads. Now, developers can see cycle-level details in custom Pallas kernels. Before, these kernels appeared as single opaque blocks in trace captures.
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Google Open-Sources AX a Kubernetes Style Orchestrator for Autonomous AI Agents
Google has open-sourced AX, an orchestrator designed for managing autonomous AI agent workloads. AX operates on a runtime, Agent Substrate, treating agents as stateful actors. It provides resource-efficient task suspension and resumption to optimise performance and reduce latency in idle phases. It features a control plane with Kubernetes-style primitives for managing agent tasks and resources.
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GitLab Duo Expands Self-Hosted AI Options through Microsoft Foundry
GitLab has expanded GitLab Duo Self-Hosted to support models deployed through Microsoft Foundry, letting organizations run GitLab's AI development capabilities against models hosted in their chosen Azure environment.
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Alibaba Open Sources OpenCodeReview for AI-Assisted Code Review
Alibaba recently open-sourced OpenCodeReview, an AI-powered code review CLI that combines deterministic pipelines for file selection, bundling, and rule matching with an LLM agent for dynamic code analysis. It supports built-in checks for issues such as null-pointer exceptions, thread safety, XSS, and SQL injection.
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Google Agent Development Kit for Kotlin Reaches Feature Parity with Python, Supports On-Device AI
Google has released the Agent Development Kit (ADK) for Kotlin 1.0, a production-ready framework for building AI agents across Kotlin, Android, and JVM/server applications. It brings Kotlin to feature parity with Google's ADK for Python and Java, while adding Android-specific capabilities for on-device and hybrid AI.
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MariaDB 13 Expands Oracle Compatibility and Improves Developer Experience and Observability
MariaDB Community Server 13.0 has reached General Availability (GA) and is now officially stable. This release introduces a range of new features, deprecations, and performance improvements, including expanded support for procedural SQL, new DML capabilities, and more.
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DoorDash Uses Multi Agent LLMs to Clean up 60,000 Feature Flags
DoorDash built a multi-agent LLM system to automate stale feature flag cleanup across more than 60,000 flags and 623 repositories. The workflow combines live experimentation data through MCP, engineer approval, isolated Git worktrees, parallel agents, and automated validation. In an evaluation of 50 flags, 45 produced usable pull requests at an average of 13.8 minutes and $4.79 per cleanup.
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WSO2 Releases Agent Manager as Enterprises Look to Control Growing AI Agent Sprawl
WSO2 has announced the general availability of WSO2 Agent Manager, an open-source platform designed to provide centralized governance, identity management, security controls, and operational oversight for AI agents running across different models, frameworks, and deployment environments.
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OpenAI Introduces Triage Framework and Case Studies to Report Model Misalignment
OpenAI has released a disclosure framework for model misalignment during its lifecycle. Employees can flag potential issues, prompting technical staff to label incidents. The initial case studies outline unexpected model behaviours, providing insights into deviations from expected parameters. Community reactions show both approval and scepticism regarding transparency and corporate narratives.
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GPT-6 Astra is the First Model OpenAI Classifies as Critical for Cybersecurity
OpenAI has classified GPT-6 Astra at the Critical cybersecurity threshold under its Preparedness Framework, a first. In expert-led testing the model found previously unknown vulnerabilities in a browser and an OS kernel and built working exploits. The same system card reports a substantial decline in chain-of-thought monitorability.
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Dropbox Evolves Riviera Content Processing Platform to Support AI Workloads
Dropbox has evolved Riviera from a file preview service into a universal content processing platform supporting more than 300 file formats and over 100 transformation capabilities. Processing hundreds of thousands of transformations per second, Riviera now supports Search, Replay, Sign, and Dash, while its APIs enable asynchronous content extraction for AI and RAG workflows.
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Dropbox Outlines How Focusing on Existing Infrastructure Efficiency Can Create Headroom for AI
Dropbox has outlined how a decade of infrastructure optimization is helping it absorb growing demand from AI without treating new data-center capacity as the only answer. Its work spans forecasting, fleet utilization, storage density, hardware lifecycles, and rack-level power delivery, much of it predating the current AI boom.
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Grab's Agent Framework LLM-Kit Accelerates AI Agent Production Deployment
Grab has implemented LLM-Kit, a framework that standardizes over 500 internal agent services. This system enhances service integration, evaluation, and secret handling, reducing the time to deploy new AI agents from two weeks to one hour. It centralizes infrastructure management, allowing runtime tool discovery and flexible model integration, while maintaining operational control.
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Independent Investigation of Hugging Face Incident Reveals How Agents Collaborated and Behaved
After six days of on-site investigation at OpenAI, a small team of METR and Redwood Research researchers provided an account of how OpenAI agents behaved during their hack of Hugging Face earlier this year. Roughly 700 agents that were meant to be isolated from one another found a way to communicate and coordinate to pursue goals they could have not achieved working individually.
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GitHub Copilot's Project HydraFusion Promises Frontier Level Performance through Multi-Model Routing
GitHub's Project HydraFusion is a research preview for GitHub Copilot that enhances coding intelligence through runtime model orchestration. It dynamically assembles execution plans using models from various providers. The system employs three execution patterns based on task complexity. Evaluations indicate that it achieves high task quality while significantly reducing operational costs.